Meanwhile, semi-supervised approaches are also applied to deal with the reject inference task.
In [1]}, the authors use a self-training algorithm to improve the performance of SVM on credit scoring.
Self-training, also known as self-labeling or decision-directed learning, is the most simple semi-supervised learning method [2]}[3]}[4]}.
This approach trains a model on approved samples, and labels rejected samples with largest default probabilities as default samples according to model predictions.
Then, the newly labeled samples are added to retrain the model, and this process continues iteratively.
Though the self-training algorithm is only used to promote SVM in [1]}, it can also promote other classifiers, such as LR, MLP and XGB.
Besides, another semi-supervised version of SVM called S3VM [6]} is also applied in reject inference.
S3VM uses approved and rejected samples to fit an optimal hyperplane with maximum margin, but have problem in fitting large-scale data [7]}.
Meanwhile, earlier works have used some statistical machine learning methods, such as Expectation-Maximization (EM) algorithm [8]}, Gaussian Mixture Models (GMM) [9]} and survival analysis [10]}, for reject inference.
Based on GMM and inspired by semi-supervised generative models [11]}[12]}, SS-GMM [7]} is proposed for modeling biased credit scoring data. The counterfactual re-weighting and semi-supervised learning are the main methods for reject inference, but neither approach considers the correlation between the learning of rejection/approval and the learning of default/non-default.
